ATLAS Collaboration(Aad, G. et al), Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Ferrer, A., Fiorini, L., et al. (2014). A neural network clustering algorithm for the ATLAS silicon pixel detector. J. Instrum., 9, P09009–34pp.
Abstract: A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fernandez Martinez, P., et al. (2015). Modelling Z -> ττ processes in ATLAS with τ-embedded Z -> μμ data. J. Instrum., 10, P09018–41pp.
Abstract: This paper describes the concept, technical realisation and validation of a largely data-driven method to model events with Z -> tau tau decays. In Z -> μμevents selected from proton-proton collision data recorded at root s = 8 TeV with the ATLAS experiment at the LHC in 2012, the Z decay muons are replaced by tau leptons from simulated Z -> tau tau decays at the level of reconstructed tracks and calorimeter cells. The tau lepton kinematics are derived from the kinematics of the original muons. Thus, only the well-understood decays of the Z boson and tau leptons as well as the detector response to the tau decay products are obtained from simulation. All other aspects of the event, such as the Z boson and jet kinematics as well as effects from multiple interactions, are given by the actual data. This so-called tau-embedding method is particularly relevant for Higgs boson searches and analyses in tau tau final states, where Z -> tau tau decays constitute a large irreducible background that cannot be obtained directly from data control samples. In this paper, the relevant concepts are discussed based on the implementation used in the ATLAS Standard Model H -> tau tau analysis of the full datataset recorded during 2011 and 2012.
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ATLAS Collaboration(Aad, G. et al), Alvarez Piqueras, D., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Fernandez Martinez, P., et al. (2016). Performance of b-jet identification in the ATLAS experiment. J. Instrum., 11, P04008–126pp.
Abstract: The identification of jets containing b hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing b hadrons are described, ranging from those based on the reconstruction of an inclusive secondary vertex or the presence of tracks with large impact parameters to combined tagging algorithms making use of multi-variate discriminants. An independent b-tagging algorithm based on the reconstruction of muons inside jets as well as the b-tagging algorithm used in the online trigger are also presented. The b-jet tagging efficiency, the c-jet tagging efficiency and the mistag rate for light flavour jets in data have been measured with a number of complementary methods. The calibration results are presented as scale factors defined as the ratio of the efficiency (or mistag rate) in data to that in simulation. In the case of b jets, where more than one calibration method exists, the results from the various analyses have been combined taking into account the statistical correlation as well as the correlation of the sources of systematic uncertainty.
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Mendez, V., Amoros, G., & Kaci, M. (2011). A Decentralized Deployment Strategy and Performance Evaluation of LCG File Catalog Service. J. Grid Comput., 9(3), 345–354.
Abstract: The LHC Computing Grid (LCG) leads by CERN, has solved with the LCG File Catalog (LFC) the major problem of scaling the data management catalog. However, additional performance issues should be faced to deploy a painless catalog service. With this aim, we present a decentralized LFC server configuration, and its performance evaluation compared with the traditional LFC deployment. A performance analysis is shown, including not only the catalog server, but also analysing the client side overhead. We find that the LFC service has in the clients a relevant workload of the overall service. The experimental results show that the proposed LFC deployment for servers and clients, improves the performance of the service.
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ATLAS Collaboration(Aad, G. et al), Amoros, G., Cabrera Urban, S., Castillo Gimenez, V., Costa, M. J., Escobar, C., et al. (2011). Study of jet shapes in inclusive jet production in pp collisions at sqrt(s)=7 TeV using the ATLAS detector. Phys. Rev. D, 83(5), 052003–29pp.
Abstract: Jet shapes have been measured in inclusive jet production in proton-proton collisions at root s = 7 TeV using 3 pb(-1) of data recorded by the ATLAS experiment at the LHC. Jets are reconstructed using the anti-k(t) algorithm with transverse momentum 30 GeV < p(T) < 600 GeV and rapidity in the region vertical bar y vertical bar < 2.8. The data are corrected for detector effects and compared to several leading-order QCD matrix elements plus parton shower Monte Carlo predictions, including different sets of parameters tuned to model fragmentation processes and underlying event contributions in the final state. The measured jets become narrower with increasing jet transverse momentum and the jet shapes present a moderate jet rapidity dependence. Within QCD, the data test a variety of perturbative and nonperturbative effects. In particular, the data show sensitivity to the details of the parton shower, fragmentation, and underlying event models in the Monte Carlo generators. For an appropriate choice of the parameters used in these models, the data are well described.
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